Zanthoxylum infructescence detection based on adaptive density clustering

Published: 26 March 2024
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To determine the Zanthoxylum yield, infructescence detection during the early fruiting stage is a prerequisite. The purpose of this research is to determine and quantify the infructescences on photos of Zanthoxylum’s early fruit-bearing branches that are gathered in their natural habitat. Consequently, a two-phase machine vision-based algorithm for identifying Zanthoxylum infructescences is proposed. First, the fruits of Zanthoxylum infructescences are extracted by extracting the histogram of oriented gradient (HOG) feature map and excess green minus excess red (ExGR) index from the branch image of the plant. The second involves roughly and adaptively classifying fruits based on the density of their position distribution. Rough clusters are then combined using an optimization model to produce the best possible clustering outcome. Experiments with normal samples demonstrate that the proposed approach receives a precision of 96.67%, a Recall of 91.07%, and an F1-score of 0.93. Compared to ADPCkNN, DBSCAN, and OPTICS algorithms, the suggested algorithm performs better in robustness and attains a higher F1-score and recall. In the meantime, its competitiveness is demonstrated in the deep learning-based method experiments. The tests demonstrate its efficacy in adaptively detecting the infructescences of branch images of Zanthoxylum.

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Ankerst, M., Breunig, M. M., Kriegel, H.-P., Sander, J. 1999. OPTICS: Ordering points to identify the clustering structure. Sigmod Rec. 28:49–60. DOI: https://doi.org/10.1145/304181.304187
Biglia, A., Zaman, S., Gay, P. 2022. 3D point cloud density-based segmentation for vine rows detection and localisation. Comput. Electron. Agr. 199:107166. DOI: https://doi.org/10.1016/j.compag.2022.107166
Caliński, T., Harabasz, J. 1974. A dendrite method for cluster analysis. Commun. Stat-Theor. M. 3:1–27. DOI: https://doi.org/10.1080/03610927408827101
Dalal, N., Triggs, B. 2005. Histograms of oriented gradients for human detection. 2005 IEEE. Comput. Soc. Conf., San Diego, CA, USA, 1:886–893. DOI: https://doi.org/10.1109/CVPR.2005.177
Ester, M., Kriegel, H.-P., Sander, J., Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. ACM SIGKDD Conference on Knowledge Discovery and Data Mining., Portland, OR, USA, 96:226–231.
Gao, F., Fang, W., Sun, X., Wu, Z., Zhao, G., Li, G., Li, R., Fu, L., Zhang, Q. 2022. A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard. Comput. Electron. Agr. 197:107000. DOI: https://doi.org/10.1016/j.compag.2022.107000
Ji, W., Peng, J., Xu, B., Zhang, T., 2023. Real-time detection of underwater river crab based on multi-scale pyramid fusion image enhancement and MobileCenterNet model. Comput. Electron. Agr. 204:107522. DOI: https://doi.org/10.1016/j.compag.2022.107522
Jocher, G., Ayush Chaurasia, Stoken, A., Borovec, J., NanoCode012, Yonghye Kwon, Kalen Michael, TaoXie, Jiacong Fang, Imyhxy, Lorna, Zeng Yifu, Wong, C., Abhiram V, Montes, D., Zhiqiang Wang, Fati, C., Jebastin Nadar, Laughing, UnglvKitDe, Sonck, V., Tkianai, YxNONG, Skalski, P., Hogan, A., Dhruv Nair, Strobel, M., Jain, M., 2022. YOLOv5 by Ultralytics, GitHub. Available from: https://github.com/ultralytics/yolov5.
Kuang, M., Zhang, L., Li, S., Yang, S., Qu, S., Dong, P. 2020. Problems and countermeasures of pepper industry development in Chongqing. South China Agriculture. 11–13.
Li, C., Tang, Y., Zou, X., Zhang, P., Lin, J., Lian, G., Pan, Y., 2022. A Novel Agricultural Machinery Intelligent Design System Based on Integrating Image Processing and Knowledge Reasoning. Appl. Sci-Basel. 12:7900. DOI: https://doi.org/10.3390/app12157900
Lin, G., Tang, Y., Zou, X., Cheng, J., Xiong, J., Fruit detection in natural environment using partial shape matching and probabilistic Hough transform[J]. Precis. Agric. 2020, 21:160-177. DOI: https://doi.org/10.1007/s11119-019-09662-w
Lu, J., Lee, W. S., Gan, H., Hu, X. 2018. Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis. Biosyst. Eng. 171:78–90. DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.009
Lv, J., Xu, H., Han, Y., Lu, W., Xu, L., Rong, H., Yang, B., Zou, L., Ma, Z. 2022. A visual identification method for the apple growth forms in the orchard. Comput. Electron. Agr. 197:106954. DOI: https://doi.org/10.1016/j.compag.2022.106954
Ma, Z., Tao, Z., Du, X., Yu, Y., Wu, C. 2021. Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method. Biosyst. Eng. 211:63–76. DOI: https://doi.org/10.1016/j.biosystemseng.2021.08.030
Maulik, U., Bandyopadhyay, S. 2002. Performance evaluation of some clustering algorithms and validity indices. IEEE T. Pattern Anal. 24:1650–1654. DOI: https://doi.org/10.1109/TPAMI.2002.1114856
Meyer, G. E., Neto, J. C., Jones, D. D., Hindman, T. W. 2004. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agr. 42:161–180. DOI: https://doi.org/10.1016/j.compag.2003.08.002
Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Adv. Neur. In. 28.
Rodriguez, A., Laio, A. 2014. Clustering by fast search and find of density peaks. Science. 344:1492–1496. DOI: https://doi.org/10.1126/science.1242072
Tan, K., Lee, W. S., Gan, H., Wang, S. 2018. Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. Biosyst. Eng. 176:59–72. DOI: https://doi.org/10.1016/j.biosystemseng.2018.08.011
Tang, Y., Chen, M., Wang, C., Luo, L., Li, J., Lian, G., Zou, X., 2020. Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review. Front. Plant. Sci. 11:510. DOI: https://doi.org/10.3389/fpls.2020.00510
Tang, Y., Zhou, H., Wang, H., Zhang, Y., 2023. Fruit detection and positioning technology for a Camellia oleifera C. Abel orchard based on improved YOLOv4-tiny model and binocular stereo vision. Expert. Syst. Appl. 211:118573. DOI: https://doi.org/10.1016/j.eswa.2022.118573
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M., 2022. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv Preprint. arXiv: 2207.02696v1. DOI: https://doi.org/10.1109/CVPR52729.2023.00721
Xu, B., Cui, X., Ji, W., Yuan, H., Wang, J., 2023. Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5. Agriculture. 13:124. DOI: https://doi.org/10.3390/agriculture13010124
Xu, Z., Huang, X., Huang, Y., Sun, H., Wan, F. 2022. A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture. Sensors. 22:Article 2. DOI: https://doi.org/10.3390/s22020682
Yaohui, L., Zhengming, M., Fang, Y. 2017. Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy. Knowl-Based. Syst. 133:208–220. DOI: https://doi.org/10.1016/j.knosys.2017.07.010
Zhang, C., Zhang, K., Ge, L., Zou, K., Wang, S., Zhang, J., Li, W. 2021. A method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and support vector machine by 3D point cloud. Sci. Hortic-Amsterdam. 278:109791. DOI: https://doi.org/10.1016/j.scienta.2020.109791
Zhang, X., Li, X., Zhang, B., Zhou, J., Tian, G., Xiong, Y., Gu, B. 2018. Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Comput. Electron. Agr. 154:165–175. DOI: https://doi.org/10.1016/j.compag.2018.09.014
Zhang, X., Toudeshki, A., Ehsani, R., Li, H., Zhang, W., Ma, R. 2022. Yield estimation of citrus fruit using rapid image processing in natural background. Smart Agricultural Technology. 2:100027. DOI: https://doi.org/10.1016/j.atech.2021.100027
Zhou, Y., Tang, Y., Zou, X., Wu, M., Tang, W., Meng, F., Zhang, Y., Kang, H., 2022. Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm. Appl. Sci-Basel. 12:12959. DOI: https://doi.org/10.3390/app122412959
Zhu, E., Ma, R. 2018. An effective partitional clustering algorithm based on new clustering validity index. Appl. Soft Comput. 71:608–621. DOI: https://doi.org/10.1016/j.asoc.2018.07.026

How to Cite

Wu, D., Zeng, S., Wang, S., Chen, Y. and Xu, Y. (2024) “<i>Zanthoxylum</i> infructescence detection based on adaptive density clustering”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1568.